Description

This file is mainly focus on the prelimianry selected sites by Beni that do not have the early GPP estimation

step1: tidy the table for GPP simulation vs GPP obs sites

step2: adopt the same way to separate out the model early simulation period as for the sites with early GPP estimation

step1: tidy the table

library(kableExtra)
library("readxl")
table.path<-"C:/Users/yluo/Desktop/CES/Data_for_use/"
my_data <- read_excel(paste0(table.path,"Info_Table_about_Photocold_project.xlsx"), sheet = "Sites_without_earlyGPPest")
# my_data %>%
# kbl(caption = "Summary of sites with early GPP estimation") %>%
#   kable_paper(full_width = F, html_font = "Cambria") %>%
#   scroll_box(width = "500px", height = "200px") #with a scroll bars
my_data %>%
  kbl(caption = "Summary of sites with early GPP estimation") %>%
  kable_classic(full_width = F, html_font = "Cambria")
Summary of sites with early GPP estimation
SiteName Delay_status Long. Lat. Period PFT Clim. N Calib. Avai.analyzed.years-spring Avai.site-years-spring Avai.analyzed.years-springawinter Avai.site-years-springawinter Reference
IT-Ren No 11.43 46.59 1998-2013 ENF Dfc 3405 Y 2002-2003,2005-2013 11 2002-2003,2005-2013 11 Montagnani et al. (2009)
RU-Ha1 No 90.00 54.73 2002-2004 GRA Dfc 567 NA no early years (2002-2004 lack early doy) 0 no early years (2002-2004 lack early doy) 0 Belelli Marchesini et al. (2007)
BE-Vie No 6.00 50.31 1996-2014 MF Cfb 4910 Y 2000-2014 15 2000-2014 15 Aubinet et al. (2001)
CH-Cha No 8.41 47.21 2005-2014 GRA Cfb 2944 NA 2006-2008,2010-2014 8 2006-2008,2010-2014 8 Merbold et al. (2014)
CH-Lae No 8.37 47.48 2004-2014 MF Cfb 3551 Y 2005-2014(2004 lack early doy) 10 2005-2014(2004 lack early doy) 10 Etzold et al. (2011)
CH-Oe1 No 7.73 47.29 2002-2008 GRA Cfb 2184 Y 2002-2008 7 2002-2008 7 Ammann et al. (2009)
DE-Gri No 13.51 50.95 2004-2014 GRA Cfb 3642 Y 2004-2014 11 2004-2014 11 Prescher et al. (2010)
DE-Obe No 13.72 50.78 2008-2014 ENF Cfb 2260 Y 2008-2014 7 2008-2014 7 NA
DE-RuR No 6.30 50.62 2011-2014 GRA Cfb 1227 Y 2012-2014 3 2012-2014 3 Post et al. (2015)
DE-Tha No 13.57 50.96 1996-2014 ENF Cfb 5141 Y 2000-2014 15 2000-2014 15 Grünwald and Bernhofer (2007)
NL-Hor No 5.07 52.24 2004-2011 GRA Cfb 2188 Y 2005,2007-2011 6 2005,2007-2010 5 Jacobs et al. (2007)
NL-Loo No 5.74 52.17 1996-2013 ENF Cfb 4671 Y 2000-2013 14 2000-2013 14 Moors (2012)
Sum NA NA NA NA NA NA 36690 NA NA 107 NA 106 NA

step2: seprate the time period when model early estimation of GPP

Part1: find the method to determine the period that with early GPP estimation

Part 2: check all the sites

    1. For Dfc:for ENF
## [1] 5

(2) For Cfb:for GRA, MF and ENF sites

  • Cfb-GRA (5 site)
## [1] 8

## [1] 7

## [1] 11

## [1] 3

## [1] 6

- Cfb-ENF (3 site)

## [1] 7

## [1] 15

## [1] 14

```

step3: save the data that label with “is_event”

Summary

steps to determine the “is_event” period

Step1: normlization for all the years in one site

#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)

Step 2:Determine the green-up period for each year(using spline smoothed values):

#followed analysis is based on the normlized “GPP_mod”time series(determine earlier sos)

  • using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs–> we can have larger analysis period

    Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)

    also for the data beyond green-up period–> the code of this steps moves to second step

    • at the end, I select the 20 days windows for the rolling mean

    Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period

    • The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data).

    • But in practise, the model performance is not always good beyond the green-up period–>I tested three data range:

      1. [peak,265/366]

      2. DoY[1, sos]& DOY[peak,365/366]

      3. [1,sos] & [eos,365/366]

    I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.

    step 5:determine the “is_event” within green-up period

    • After some time of consideration, I took following crition to determine the “is_event”:

      1. during the growing season period (sos,eos)–>the data with GPP biases bigger than 2 SD are classified as the “GPP overestimation points”

      2. For “GPP overstimation points”, thoses are air temparture is less than 10 degrees will be classified as the “is_event”. I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)

      References:

      Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052

      Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108

    step 6:Evaluation “is_event”–>visualization and stats

    • two ways to evaluate if “is_event” is properly determined:

    1. visulization

    2. stats: \[ Pfalse = \frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} \]